In the autonomous navigation technology, the scene where the unmanned aerial vehicle flies over is surveilled by sensors in the unmanned aerial vehicle, and then the unmanned aerial vehicle accomplishes autonomous localization and flight path analysis according to the surveillance result. Therefore, this technology is widely used in the military and scientific research fields. Nowadays, with the popularization of low-cost sensors and the improvement of embedded computing technology, the autonomous navigation technology is gradually extended to the civil and commercial fields from the military and scientific research fields. However, in the indoor scenarios, there are still two problems existing in the autonomous navigation technology in the related art.
Firstly, the unmanned aerial vehicle in the related art accomplishes its own spatial positioning in the scene by using mainly the GPS positioning technology. However, in the complex indoor scene, the unmanned aerial vehicle cannot effectively use the GPS positioning technology for the spatial positioning because of the influence of factors such as the building blocking, which makes the unmanned aerial vehicle not able to effectively carry out the autonomous navigation flight in the complex indoor scene.
Secondly, in the complex indoor scene, the autonomous navigation technology requires the environment map with greater precision. However, the SLAM algorithm in the related art can only establish the sparse map model with an error less than 5% of the whole environment scale. Meanwhile, the laser scanning system which may establish the high precision map model with an error less than 1% of the whole environment scale is not suitable for the unmanned aerial vehicle which flies in the inner environment. Therefore, there is also a need to improve the method for establishing the high precision map model in the indoor environment.